Wearable Sensor-Based Behavioral Anomaly Detection in Smart Assisted Living Systems

• Published in: IEEE transactions on automation science and engineering Vol. 12; no. 4; pp. 1225 - 1234
• Main Authors: Zhu, Chun, Sheng, Weihua, Liu, Meiqin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anomalies; Anomaly detection; Artificial intelligence; Assisted living; Assisted living facilities; Automation; Bayesian analysis; Dynamical systems; Dynamics; Elder care; Elderly people; Human behavior; Intelligent sensors; Laplace transforms; Robot sensing systems; Sensors; smart home; Smart homes; Wearable; Wearable computers; wearable computing; Wearable sensors
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2015.2474743

Detecting behavioral anomalies in human daily life is important to developing smart assisted-living systems for elderly care. Based on data collected from wearable motion sensors and the associated locational context, this paper presents a coherent anomaly detection framework to effectively detect different behavioral anomalies in human daily life. Four types of anomalies, including spatial anomaly, timing anomaly, duration anomaly, and sequence anomaly, are detected using a probabilistic theoretical framework. This framework is based on complex activity recognition using dynamic Bayesian network modeling. The maximum-likelihood estimation algorithm and Laplace smoothing are used in learning the parameters in the anomaly detection model. We conducted experimental evaluation in a mock apartment environment, and the results verified the effectiveness of the proposed framework. We expect that this behavioral anomaly detection system can be integrated into future smart homes for elderly care.